4 papers
Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language
Michelle Stegeman, Lena Philipp, Fennie van der Graaf +19
Medical foundation models show promise to learn broadly generalizable features from large, diverse datasets. This could be the base for reliable cross-modality generalization and r…
"No negatives needed": weakly-supervised regression for interpretable tumor detection in whole-slide histopathology images
Marina D'Amato, Jeroen van der Laak, Francesco Ciompi
Accurate tumor detection in digital pathology whole-slide images (WSIs) is crucial for cancer diagnosis and treatment planning. Multiple Instance Learning (MIL) has emerged as a wi…
Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation
Artur Jurgas, Marek Wodzinski, Marina D'Amato +3
The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Ove…
Benchmarking Hierarchical Image Pyramid Transformer for the classification of colon biopsies and polyps in histopathology images
Nohemi Sofia Leon Contreras, Marina D'Amato, Francesco Ciompi +5
Training neural networks with high-quality pixel-level annotation in histopathology whole-slide images (WSI) is an expensive process due to gigapixel resolution of WSIs. However, r…